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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto and Sergio S. Furuie. 2011. Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano and Walter Kropatsch, eds. 14th International Conference on Computer Analysis of Images and Patterns. Berlin, Springer-Verlag Berlin Heidelberg, 520–528. (LNCS.)
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Patricia Marquez, Debora Gil and Aura Hernandez-Sabate. 2011. A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth. IEEE International Conference on Computer Vision – Workshops. Barcelona (Spain), IEEE, 2042–2049.
Abstract: Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems.
Keywords: IEEE International Conference on Computer Vision – Workshops
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David Vazquez, Antonio Lopez, Daniel Ponsa and Javier Marin. 2011. Virtual Worlds and Active Learning for Human Detection. 13th International Conference on Multimodal Interaction. New York, NY, USA, USA, ACM DL, 393–400.
Abstract: Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid.
Keywords: Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning
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Andrew Nolan, Daniel Serrano, Aura Hernandez-Sabate, Daniel Ponsa and Antonio Lopez. 2013. Obstacle mapping module for quadrotors on outdoor Search and Rescue operations. International Micro Air Vehicle Conference and Flight Competition.
Abstract: Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.
Keywords: UAV
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Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo and Josep Llados. 2011. Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval. In P. Clough and 6 others, eds. 33rd European Conference on Information Retrieval. Berlin, Springer, 314–325. (LNCS.)
Abstract: In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
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David Vazquez, Antonio Lopez, Daniel Ponsa and Javier Marin. 2011. Cool world: domain adaptation of virtual and real worlds for human detection using active learning. NIPS Domain Adaptation Workshop: Theory and Application. Granada, Spain.
Abstract: Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
Keywords: Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
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Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo and Ramon Lopez de Mantaras. 2011. The IIIA30 MObile Robot Object Recognition Datset. 11th Portuguese Robotics Open.
Abstract: Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones.
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Mohammad Rouhani and Angel Sappa. 2011. Implicit B-Spline Fitting Using the 3L Algorithm. 18th IEEE International Conference on Image Processing.893–896.
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Marçal Rusiñol, David Aldavert, Ricardo Toledo and Josep Llados. 2011. Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. 11th International Conference on Document Analysis and Recognition.63–67.
Abstract: In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts.
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G.D. Evangelidis, Ferran Diego, Joan Serrat and Antonio Lopez. 2011. Slice Matching for Accurate Spatio-Temporal Alignment. In ICCV Workshop on Visual Surveillance.
Abstract: Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works.
Keywords: video alignment
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